Search results “Data mining instance definition in java”
Data Mining with Weka (1.6: Visualizing your data)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 62505 WekaMOOC
Naive Bayes w/ JAVA - Tutorial 01
Website + download source code @ http://www.zaneacademy.com
Views: 3963 zaneacademy
What is INSTANCE SELECTION? What does INSTANCE SELECTION mean? INSTANCE SELECTION meaning - INSTANCE SELECTION definition - INSTANCE SELECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Instance selection (or dataset reduction, or dataset condensation) is an important Data pre-processing step that can be applied in many Machine learning (or Data mining) tasks. Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a reduction of the computational resources that are necessary for performing the learning process. Algorithms of instance selection can also be applied for removing noisy instances, before applying learning algorithms. This step can improve the accuracy in classification problems. Algorithm for instance selection should identify a subset of the total available data to achieve the original purpose of the data mining (or machine learning) application as if the whole data had been used. Considering this, the optimal outcome of IS would be the minimum data subset that can accomplish the same task with no performance loss, in comparison with the performance achieved when the task is performed using the whole available data. Therefore, every instance selection strategy should deal with a trade-off between the reduction rate of the dataset and the classification quality. The literature provides several differente algorithms for instance selection. They can be distinguished from each other according to several different criteria. Considering this, instance selection algorithms can be grouped in two main classes, according to what instances they select: algorithms that preserve the instances at the boundaries of classes and algorithms that preserve the internal instances of the classes. Within the category of algorithms that select instances at the boundaries it is possible to cite DROP3, ICF and LSBo. On the other hand, within the category of algorithms that select internal instances it is possible to mention ENN and LSSm. In general, algorithm such as ENN and LSSm are used for removing harmful (noisy) instances from the dataset. They do not reduce the data as the algorithms that select border instances, but they remove instances at the boundaries that have negative impact in the data ming task. They can be used bay other instance selection algorithms, as a filtering step. For example, the ENN algorithm is used by DROP3 as the first step, and the LSSm algorithm is used by LSBo. There is also another group os algorithms that adopt different selection criteria. For example, the algorithms LDIS and CDIS select the densest instances in a given arbitrary neighborhood. The selected instances can include both, border and internal instances. The LDIS and CDIS algorithms are very simple and select subsets that are very representative of the original dataset. Besides that, since they search by the representative instances in each class separately, they are faster (in terms of time complexity and effective running time) than other algorithms, such as DROP3 and ICF.
Views: 117 The Audiopedia
Java Data Sci Soltn-Big Data & Visualizatn: Cluster Data Point Using K-means Algorithm| packtpub.com
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2vbDmvu]. In this video, we will use the K-means algorithm to cluster or group data points of a dataset together. • Load the cpu dataset • Develop the clusterer • Get each instance and cluster number using the K-means algorithm For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 1108 Packt Video
Meta data  in 5 mins hindi
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 85594 Last moment tuitions
Regular Expressions (Regex) Tutorial: How to Match Any Pattern of Text
In this regular expressions (regex) tutorial, we're going to be learning how to match patterns of text. Regular expressions are extremely useful for matching common patterns of text such as email addresses, phone numbers, URLs, etc. Almost every programming language has a regular expression library, so learning regular expressions with not only help you with finding patterns in your text editors, but also you'll be able to use these programming libraries to search for patterns programmatically as well. Let's get started... The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Regular-Expressions Python Regex Tutorial: https://youtu.be/K8L6KVGG-7o If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/
Views: 127762 Corey Schafer
Latanya Sweeney: When anonymized data is anything but anonymous
Relatively simple data science experiments can yield major insights and have a significant impact. Many experiments in data science are expensive and time consuming to pursue. But Latanya Sweeney, professor of government and technology at Harvard University, has shown that even relatively simple studies conducted by students can have a significant impact on public policy and society. As a student in the 1990s, Sweeney discovered that by applying a couple of filters to a database containing supposedly anonymized health records of Massachusetts state employees, she was able to identify the medical history of Gov. William Weld. That simple experiment led to a broader conclusion: Most people in the United States are the only ones in their ZIP code with a particular date of birth, which means it is relatively easy to discover their identities in much the same way Sweeney found Weld’s history. “That impact -- the ability to have a simple experiment and have dramatic impact – was huge, and something that stayed with me forever. That simple experiment was quoted in the preamble of HIPAA and the rewrite of privacy laws around the world,” Sweeney said during a talk at this year’s Women in Data Science (WiDS) conference at Stanford University. Over the years, Sweeney and her associates have flagged numerous instances of flaws in public databases that have caused significant harm. And they’ve found instances where data sources have been misused or applied in a discriminatory manner. A query by a reporter prompted Sweeney to look for a correlation between names typically given to African-Americans and online ads mentioning arrest records. She found it. Online searches containing a name that sounds like it belongs to a black person were 80 percent more likely to generate an ad mentioning arrest records than searches for stereotypically white names. “Somebody goes online to see what they can find out about you and Googles your name. And if the ads are popping up implying that you have an arrest record, then in fact, you’re at a disadvantage. It’s not about the intent or whether it was intended,” said Sweeney, who was chief technology officer for the Federal Trade Commission from January 2014 until December 2014. A study by Sweeney’s students found that a major SAT tutoring company charged higher prices to Asians. And Airbnb modified its pricing policies after the students found price discrimination against certain groups. Referring to the examples she gave during her talk, Sweeney said: “I like to think I’m really smart, but the truth is these are really simple experiments. But they have profound impact because they empower someone else to be able to do their job better.”
K-means clustering: how it works
Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 431167 Victor Lavrenko
Data Mining with Weka (2.2: Training and testing)
Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 67308 WekaMOOC
WEKA API 18/19: Association Rules (the Apriori Algorithm)
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.imperial.ac.uk/people/n.sadawi Using WEKA in java
Views: 15230 Noureddin Sadawi
Advanced Data Mining with Weka (4.6: Application: Image classification)
Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 6287 WekaMOOC
WEKA API 4/19: Filtering Attributes
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.imperial.ac.uk/people/n.sadawi Using WEKA in java
Views: 12005 Noureddin Sadawi
KD tree algorithm: how it works
[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data instance, we find the matching leaf of the K-D tree, and compare the instance to all the training point in that leaf.
Views: 68995 Victor Lavrenko
Object Region Mining With Adversarial Erasing: A Simple Classification to ...
Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, Shuicheng Yan We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems. Classification networks are only responsive to small and sparse discriminative regions from the object of interest, which deviates from the requirement of the segmentation task that needs to localize dense, interior and integral regions for pixel-wise inference. To mitigate this gap, we propose a new adversarial erasing approach for localizing and expanding object regions progressively. Starting with a single small object region, our proposed approach drives the classification network to sequentially discover new and complement object regions by erasing the current mined regions in an adversarial manner. These localized regions eventually constitute a dense and complete object region for learning semantic segmentation. To further enhance the quality of the discovered regions by adversarial erasing, an online prohibitive segmentation learning approach is developed to collaborate with adversarial erasing by providing auxiliary segmentation supervision modulated by the more reliable classification scores. Despite its apparent simplicity, the proposed approach achieves 55.0% and 55.7% mean Intersection-over-Union (mIoU) scores on PASCAL VOC 2012 val and test sets, which are the new state-of-the-arts.
Java Core lecture 4  - Variables and Data Types in Java
email id - [email protected] facebook page - www.facebook.com/proatcode/ instagram - @proatcode
Views: 18 ProAtCode Tech
What Is Metadata? (Overview of meta data)
http://zerotoprotraining.com What Is Metadata? Metadata Overview Category: Programming Tags: Metadata Overview
Views: 19578 HandsonERP
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. Support us on Patreon, so we can bring you more cool Machine and Deep Learning Content :) https://www.patreon.com/ArduinoStartups ------------------------------------------------------------ To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 32051 Augmented Startups
WEKA API 2/19: Loading and Saving Data
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.imperial.ac.uk/people/n.sadawi Using WEKA in java
Views: 18558 Noureddin Sadawi
Weka Tutorial 02: Data Preprocessing 101 (Data Preprocessing)
This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 155004 Rushdi Shams
Datamining Algorithm with Hadoop Cluster  on AWS  Part1
Running data mining algorithm for finding frequent items from large data on AWS Cloud
Views: 130 Sanket Thakare
Data Mining with Weka (1.4: Building a classifier)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Building a classifier http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 74630 WekaMOOC
Privacy Preserving DataMining
Google TechTalks July 28, 2006 Matthew Roughan joined the School of Applied Mathematics at the University of Adelaide in February 2004, where he is interested in the area of design, and installation of Internet measurement equipment, and the analysis and modeling of Internet measurement data. ABSTRACT The rapid growth of the Internet over the last decade has been startling. However, efforts to track its growth have often fallen afoul of bad data --- for instance, how much traffic does the Internet now carry? The problem is not that the data is technically hard to obtain, or that it does not exist, but rather that the data is not shared. Obtaining an overall picture requires data from multiple...
Views: 3396 GoogleTechTalks
Introduction to Data Mining: Data Attributes (Part 2)
This video is part three of the introduction to the data mining vocabulary. Explaining important attribute classes -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Ljk0 See what our past attendees are saying here: https://hubs.ly/H0f8L-10 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 6492 Data Science Dojo
How kNN algorithm works
In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
Views: 348086 Thales Sehn Körting
WEKA API 14/19: Making Predictions (Classification)
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.imperial.ac.uk/people/n.sadawi Using WEKA in java
Views: 16856 Noureddin Sadawi
Java Implementation of K-Nearest Neighbors (kNN) Classifier 1/2
The code can be found here: www.imperial.ac.uk/people/n.sadawi Go to Tutorials and then Machine Learning section!
Views: 31635 Noureddin Sadawi
Weka Tutorial 22: Setting Class Attribute (Data Preprocessing)
This tutorial tells you what to do to take your class feature to the very end of your feature list using Weka Explorer.
Views: 17115 Rushdi Shams
UML Class Diagram Tutorial
Learn how to make classes, attributes, and methods in this UML Class Diagram tutorial. There's also in-depth training and examples on inheritance, aggregation, and composition relationships. UML (or Unified Modeling Language) is a software engineering language that was developed to create a standard way of visualizing the design of a system. And UML Class Diagrams describe the structure of a system by showing the system’s classes and how they relate to one another. This tutorial explains several characteristics of class diagrams. Within a class, there are attributes, methods, visibility, and data types. All of these components help identify a class and explain what it does. There are also several different types of relationships that exist within UML Class Diagrams. Inheritance is when a child class (or subclass) takes on all the attributes and methods of the parent class (or superclass). Association is a very basic relationship where there's no dependency. Aggregation is a relationship where the part can exist outside the whole. And finally, Composition is when a part cannot exist outside the whole. A class would be destroyed if the class it's related to is destroyed. Further UML Class Diagram information: https://www.lucidchart.com/pages/uml/class-diagram —— Learn more and sign up: http://www.lucidchart.com Follow us: Facebook: https://www.facebook.com/lucidchart Twitter: https://twitter.com/lucidchart Instagram: https://www.instagram.com/lucidchart LinkedIn: https://www.linkedin.com/company/lucidsoftware —— Credits for Photos with Attribution Requirements Tortoise - by Niccie King - http://bit.ly/2uHaL1G Otter - by Michael Malz - http://bit.ly/2vrVoYt Slow Loris - by David Haring - http://bit.ly/2uiBWxg Creep - by Poorna Kedar - http://bit.ly/2twR4K8 Visitor Center - by McGheiver - http://bit.ly/2uip0Hq Lobby - by cursedthing - http://bit.ly/2twBWw9
Views: 510281 Lucidchart
2012-04-11 - : K-Anonymity in Social Networks: A Clustering Approach - CERIAS Security Seminar
Recorded: 04/11/2012 CERIAS Security Seminar at Purdue University : K-Anonymity in Social Networks: A Clustering Approach Traian Truta, Northern Kentucky University The proliferation of social networks, where individuals share private information, has caused, in the last few years, a growth in the volume of sensitive data being stored in these networks. As users subscribe to more services and connect more with their friends, families, and colleagues, the desire to use this information from the networks has increased. Online social interaction has become very popular around the globe and most sociologists agree that this will not fade away. Social network sites gather confidential information from their users (for instance, the social network site PacientsLikeMe collects confidential health information) and, as a result, social network data has begun to be analyzed from a different, specific privacy perspective. Since the individual entities in social networks, besides the attribute values that characterize them, also have relationships with other entities, the risk of disclosure increases. In this talk we present a greedy algorithm for anonymizing a social network and a measure that quantifies the information loss in the anonymization process due to edge generalization. Traian Marius Truta is an associate professor of Computer Science at Northern Kentucky University. He received his Ph.D. in computer science from Wayne State University in 2004. His major areas of expertise are data privacy and anonymity, privacy in statistical databases, and data management. He has served on the program committee of various conferences such as International Conference on Database and Expert Systems Applications (DEXA), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), ACM Symposium of Applied Computing (SAC), and International Symposium on Data, Privacy, and E-Commerce (ISDPE). He received the Yahoo Research! Best Paper Award for Privacy, Security, and Trust in KDD 2008 (PinKDD) for the paper �A Clustering Approach for Data and Structural Anonymity in Social Networks� in 2008. For more information, including the list of research publications please see: http://www.nku.edu/~trutat1/research.html. (Visit: www.cerias.purude.edu)
Views: 2834 ceriaspurdue
Weka Tutorial 15: Java API 101 (Application)
In this tutorial, I showed how to interact with the Weka API for the first time with a simple Java code. In this code, I have loaded an ARFF file called 2.arff and then used Naive Bayes classifier with a 10 fold CV setup. I showed the standard output of Weka on the Eclipse output as well as the F-score, precision and recall of the 10 fold CV.
Views: 50308 Rushdi Shams
Java kursus / undervisning #21 | Public, Private og Protected data i Java
Se flere videoer på http://www.nemprogrammering.dk/
Views: 917 NemProgrammering
k nearest neighbor (kNN): how it works
[http://bit.ly/k-NN] The k-nearest neighbor (k-NN) algorithm is based on the intuition that similar instances should have similar class labels (in classification) or similar target values (regression). The algorithm is very simple, but is capable of learning highly-complex non-linear decision boundaries and regression functions. On the downside, the algorithm is computationally expensive, and is prone to overfitting.
Views: 78605 Victor Lavrenko
Append & Merge Data in weka tool using CLI
blogs www.weka.sourceforge.net/doc.dev/weka/core/Instances.html http://weka.8497.n7.nabble.com/merging-several-data-sets-td19940.html This tutorial shows how to append and merge 2 or more than 2 ARFF files in weka data mining tool. Append & Merge Data in weka tool using CLI Steps: Syntax (i)java weka.core,Instances append Sourcepath1 Sourcepath2 "here write angle braces" Destination path Where, Sourcepath1 and Sourcepath2 must required same no. of @attribute with same datatype(s) (ii)java weka.core,Instances merge Sourcepath1 Sourcepath2 "write angle bracket" Destinationpath Where, Sourcepath1 and Sourcepath2 must required different name of @attribute Start weka Click on Select Simple CLI Within this dialogue box in the bottom part type Apply to syntax(i) Start weka Click on Select Simple CLI Within this dialogue box in the bottom part type Apply to syntax(ii)
Views: 776 Sweven Developers
Weka Tutorial 14: The Java API with Eclipse (Application)
In this tutorial I showed how you can download and incorporate the Weka API with Eclipse Java IDE. The download link for the api is http://www.cs.waikato.ac.nz/ml/weka/
Views: 36678 Rushdi Shams
WEKA API 1/19: Introduction and Setting Up Eclipse
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.imperial.ac.uk/people/n.sadawi Using WEKA in java
Views: 44572 Noureddin Sadawi
WEKA API 16/19: Saving and Loading Models
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.imperial.ac.uk/people/n.sadawi Using WEKA in java
Views: 5773 Noureddin Sadawi
Getting Started with Weka - Machine Learning Recipes #10
Hey everyone! In this video, I’ll walk you through using Weka - The very first machine learning library I’ve ever tried. What’s great is that Weka comes with a GUI that makes it easy to visualize your datasets, and train and evaluate different classifiers. I’ll give you a quick walkthrough of the tool, from installation all the way to running experiments, and show you some of what it can do. This is a helpful library to have while you’re learning ML, and I still find it useful today to experiment with new datasets. Note: In the video, I quickly went through testing. This is an important topic in ML, and how you design and evaluate your experiments is even more important than the classifier you use. Although I publish these videos at turtle speed, I’ve started working on an experimental design one, and that’ll be next! Also, we will soon publish some testing tips and best practices on tensorflow.org (https://goo.gl/nZcS5R). Links from the video: Weka → https://goo.gl/2TYjGZ Ready to use datasets → https://goo.gl/PM8DtH More on evaluating classifiers, particularly in the medical domain → https://goo.gl/TwTYyk Check out the Machine Learning Recipes playlist → https://goo.gl/KewA03 Follow Josh on Twitter → https://twitter.com/random_forests Subscribe to the Google Developers channel → http://goo.gl/mQyv5L
Views: 45694 Google Developers
Data Mining with Weka (1.2: Exploring the Explorer)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 2: Exploring the Explorer http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 85608 WekaMOOC
Weka Tutorial 08: Numeric Transform (Data Preprocessing)
Weka provides a filter called NumericTransform so that you can use the Java.Lang.Math class methods to transform your feature values. This is particularly useful as for some classification algorithms you will see that they perform better with integer values than real numbers or vice versa.
Views: 29226 Rushdi Shams
WEKA API 19/19: Clustering in WEKA
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.imperial.ac.uk/people/n.sadawi Using WEKA in java
Views: 12819 Noureddin Sadawi
What is Text Mining?
An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 42325 Elsevier
Agglomerative Clustering: how it works
[http://bit.ly/s-link] Agglomerative clustering guarantees that similar instances end up in the same cluster. We start by having each instance being in its own singleton cluster, then iteratively do the following steps: (1) find a pair or most similar clusters and (2) merge them into a single cluster. The result is a tree structure called the dendrogram.
Views: 89721 Victor Lavrenko
Data Mining with Weka (4.4: Logistic regression)
Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Logistic regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 30254 WekaMOOC
Weka Tutorial 03: Classification 101 using Explorer (Classification)
In this tutorial, classification using Weka Explorer is demonstrated. This is the very basic tutorial where a simple classifier is applied on a dataset in a 10 Fold CV. For more variations of classification, watch out other tutorials on this channel.
Views: 144445 Rushdi Shams
Advanced Data Mining with Weka (2.3: The MOA interface)
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: The MOA interface http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3249 WekaMOOC
What Is A Class In Object Oriented Programming?
Object oriented programming (c#) introduction to object concepts (oop) and classes goodbye, charles scalfani medium. What is ? (the java tutorials learning the what Definition from whatis. It serves as a template for creating, or instantiating, specific object oriented programming has become the most widely used approach to software development. An analogy when we call the class object, a new instance of is created, and __init__ method on this object immediately executed with all parameters programming objects can ignore anything not directly concerning behaviour or state an object; We instead turn our attention to classes terms are sometimes used interchangeably, but in fact, describe type objects, however according oriented design should have one, only reason change python language, which means it manipulates constructs called. Ex40 modules, classes, and objects learn python the hard way. Object oriented programming concepts objects and classes adobe devnet actionscript oop. Classes and objects are the two main aspects of object oriented programming. Thus, an object is a specific instance of class; It contains real values instead variables. Think of an object in oriented programming, is extensible program code template for creating objects, providing initial values state (member variables) and implementations behavior functions or methods). Classes, objects, and methods caml. Object oriented interitance atomic object. Object oriented programming concepts objects and classes adobe. This means there is a construct in python called class that lets you structure your software the classes toward top of an inheritance hierarchy tend to be abstract at design level (interfaces); Programming by drag and drop (components). Learn about the core concepts of object oriented may 8, 2001 if you're not familiar with programming, some can be hard to understand, especially a longtime dec 19, 2016. Classes object oriented programming in python 1 documentation. A class creates a new type where objects are instances of the. In these languages, that creates classes is called metaclass in object oriented terms, we say your bicycle an instance of the syntax java programming language will look new to you, but, template definition method s and variable particular kind. Class (computer programming) wikipedia. Object oriented programming tutorial classes aonaware. Class definition the tech terms computer dictionary. A new project came along and i thought back to that class was so aug 21, 2017 object oriented programming (oop) is a structure where programs are organized around objects as opposed action logic python called an 'object language. Object oriented programming a byte of python. Googleusercontent search. You can think of an object as a single jul 23, 2016 i've been programming in oriented languages for decades. Object oriented programming 1 classes and objects youtube. Object oriented programming objects, classes & methods video intro to oop understanding and ob
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Decision Trees 02 (JAVA Tutorial) - Build tree w/ information gain
Website + download source code @ http://www.zaneacademy.com | Decision Trees 01 w/ JAVA @ https://youtu.be/zhY92L2i5AE | Decision Trees 01 w/ Python @ https://youtu.be/303yUAhD_RE
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k-NN 2: classification and regression
[http://bit.ly/k-NN] The k-NN algorithm operates as follows. For a new test instance, we first compute its distance to all the N training instances, and keep a small number k of nearest neighbours. For classification, we then predict the most dominant class among the k neighbours. For regression, we compute the mean target value across the k neighbours.
Views: 18679 Victor Lavrenko
Machine Learning - Collaborative Filtering & Its Challenges
Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
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